Validity Arguments for Diagnostic Assessment Using Automated Writing Evaluation

Two examples demonstrate an argument-based approach to validation of diagnostic assessment using automated writing evaluation (AWE). Criterion ®, was developed by Educational Testing Service to analyze students’ papers grammatically, providing sentence-level error feedback. An interpretive argument was developed for its use as part of the diagnostic assessment process in undergraduate university English for academic purposes (EAP) classes. The Intelligent Academic Discourse Evaluator (IADE) was developed for use in graduate EAP university classes, where the goal was to help students improve their discipline-specific writing. The validation for each was designed to support claims about the intended purposes of the assessments. We present the interpretive argument for each and show some of the data that have been gathered as backing for the respective validity arguments, which include the range of inferences that one would make in claiming validity of the interpretations, uses, and consequences of diagnostic AWE-based assessments

Vocabulary Learning Support System based on Automatic Image Captioning Technology

7th International Conference, DAPI 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Orlando, FL, USA, July 26–31, 2019, ProceedingsLearning context has evident to be an essential part in vocabulary development, however describing learning context for each vocabulary is considered to be difficult. In the human brain, it is relatively easy to describe learning contexts using pictures because pictures describe an immense amount of details at a quick glance that text annotations cannot do. Therefore, in an informal language learning system, pictures can be used to overcome the problems that language learners face in describing learning contexts. The present study aimed to develop a support system that generates and represents learning contexts automatically by analyzing the visual contents of the pictures captured by language learners. Automatic image captioning, a technology of artificial intelligence that connects computer vision and natural language processing is used for analyzing the visual contents of the learners’ captured images. A neural image caption generator model called Show and Tell is trained for image-to-word generation and to describe the context of an image. The three-fold objectives of this research are: First, an intelligent technology that can understand the contents of the picture and capable to generate learning contexts automatically; Second, a leaner can learn multiple vocabularies by using one picture without relying on a representative picture for each vocabulary, and Third, a learner’s prior vocabulary knowledge can be mapped with new learning vocabulary so that previously acquired vocabulary be reviewed and recalled while learning new vocabulary

Adaptive and intelligent mentoring to increase user attentiveness in learning activities

In the past decades intelligent mentoring systems have rapidly increased. In e-learning environment there has been an exponential growth in technological development environments and number of users that are addressed, hence an intelligent mentoring system should capture the user’s attention in order to improve results when focused in (e)learning tasks (i.e. serve both as a support of presence lessons and for distance form of studies – e-learning). It is important to note that the process of teaching-learning requires an interaction between the different actors involved: the tutor, the student, the expert domain and the learning environment or interface. In this paper we propose an innovative approach of an intelligent mentoring system that monitors the user’s biometric behaviour and measures his/her attention level during e-learning activities. Additionally, a machine learning categorisation model is presented that monitors students’ activity during school lessons. Nowadays computers are used as important working tools in many places, where we intend to use non-invasive methods of intelligent orientation through the observation of the user’s interaction with the computer.This work has been supported by: SENESCYT - Universidad do Minho and Secretaría de Educación Superior, Ciencia, Tecnología e Innovación within the Project: SENESCYT-SDFC-DSEFC-2017-2855-O; Part-funded by ERDF European Regional Development Fund and by National Funds through the FCT Portuguese Foundation for Science and Technology within project NORTE-01-0247-FEDER-017832. The work of Filipe Gonçalves is supported by a FCT grant with the reference ICVS-BI-2016-005

The Role of Universal Constraints in Language Acquisition

International audienc